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The assessment of rainfall erosivity is often hindered by the limited availability of high-resolution rainfall data. A large dataset, comprising 10-minute rainfall data collected over the last two decades from 335 rain gauges across three regions of southern Italy, was utilized in this study to estimate benchmark values of mean annual rainfall erosivity according to the Revised Universal Soil Loss Equation. A set of ten existing simplified models based on coarser resolution rainfall data (from daily to annual) were compared to two newly developed empirical models based on daily-resolution data. The first proposed model uses two compound meteorological predictors, namely the rainfall episodicity and intensity indices, derived from the mean annual rainfall, the Gini's coefficient, and the mean annual number of rainy days. The second model integrates the previous one with geographic and topographic covariates, including latitude, elevation, and minimum distance to the coastline. We evaluated and compared the performances of all models using various metrics, including the Root Mean Square Error (RMSE), the mean error, the adjusted coefficient of determination, the Kling-Gupta efficiency, and the Akaike information criterion. All the performance indices showed how the newly developed models outperformed the ten existing recalibrated equations, obtaining a reduction in absolute percentage error from 27 to 18 %. Our extensive dataset enabled robust calibration and validation of existing and new simplified models, paving the way for an ensemble modeling approach that enhances model transferability in data-scarce environments under similar precipitation regimes.
Woldegebrael et al. (Mon,) studied this question.